... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

This chapter deals with the problem of whole-image categorization. We may want to classify a photograph based on a high-level semantic attribute (e.g., indoor or outdoor), scene type (forest, street, office, etc.), or object category (car, face, etc.). Our philosophy is that such global image tasks can be approached in a holistic fashion: It should be possible to develop image representations that use low-level features to directly infer high-level semantic information about the scene without going through the intermediate step of segmenting the image into more “basic” semantic entities. For example, we should be able to recognize that an image contains a beach scene without first segmenting and identifying its separate components, such as sand, water, sky, or bathers. This philosophy is inspired by psychophysical and psychological evidence that people can recognize scenes by considering them in a “holistic ” manner, while overlooking most of the details of the constituent objects (Oliva and Torralba, 2001). It has been shown that human subjects can perform high-level categorization tasks extremely rapidly

Shot boundary detection The shot boundary detection system in 2007 is basically the same as that of last year. We make three major modifications in the system of this year. First, CUT detector and GT detector use block based RGB color histogram with the different parameters instead of the same ones. Secondly, we add a motion detection module to the GT detector so that we can remove the false alarms caused by camera motion or large object movements. Finally, we add a post-processing module based on SIFT feature after both CUT and GT detector. The evaluation results show that all these modifications bring performance improvements to the system. The brief introduction to each run is shown in the following table: Run_id Description Thu01 Baseline system: RGB4_48 for CUT and GT detector, no motion detector, no sift post-processing, only using development set of 2005 as training set Thu02 Same algorithm as thu01, but with RGB16_48 for CUT detector, RGB4_48 for GT detector Thu03 Same algorithm as thu02, but with SIFT post-processing for CUT Thu04 Same algorithm as thu03, but with Motion detector for GT

High level feature extraction ID MAP Training set Testing set Brief description run1 0.116 LIG & CAS 1frame/shot & 3frame/shot baseline+keypoint run2 0.123 LIG & CAS 3frame/shot baseline run3 0.057